Therapeutic Drug Monitoring for Drugs Used in the Treatment of Substance-Related Disorders: Literature Review Using a Therapeutic Drug Monitoring Appropriateness Rating Scale
Bibliographic record
Abstract
BACKGROUND: The efficacy of drugs for the treatment of substance-related disorders is moderate at best. Therapeutic drug monitoring (TDM) could be an instrument to improve outcomes. Because TDM for most of those drugs is not established, the authors reviewed the literature and built a rating scale to detect the potential added value of TDM for these pharmacologic agents. METHODS: A literature search was performed for acamprosate, bupropion, buprenorphine, clomethiazole, disulfiram, methadone, naltrexone, and varenicline. The rating scale included 22 items and was divided in five categories: efficacy, toxicity, pharmacokinetics, patient characteristics, and cost-effectiveness. Three reference substances with established TDM were similarly assessed for comparison: clozapine, lithium, and nortriptyline. The three reference substances achieved scores of 15, 12, and 14 points, respectively. RESULTS: Drugs for treatment of substance-related disorders achieved 3 to 17 points, 17 for methadone, 11 for buprenorphine, 10 for disulfiram, also 10 for naltrexone for the indication opioid-dependence and 9 for the indication alcohol dependence as well as bupropion, 7 points for acamprosate, 6 points for clomethiazole, and 3 for varenicline. CONCLUSIONS: It is concluded that systematic evaluation of drug- and patient-related variables with the new rating scale can estimate the appropriateness of TDM. Because their rating revealed similar scores as the three reference drugs, it is proposed that TDM should be established for bupropion, buprenorphine, disulfiram or a metabolite, methadone, and naltrexone. An objective rating of drug- and patient-related characteristics could help laboratories focus their method development on the most likely drugs to require TDM along with a thorough drug use evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".